Rough Diamonds in Natural Language Learning

Rough Set Approach to Domain Knowledge Approximation

Classification systems working on large feature spaces, despite extensive learning, often perform poorly on a group of atypical samples. The problem can be dealt with by incorporating domain knowledge about samples being recognized into the learning process. We present a method that allows to perform this task using a rough approximation framework. We show how human expert’s domain knowledge ex...

Stable Rough Extreme Learning Machines for the Identification of Uncertain Continuous-Time Nonlinear Systems

‎Rough extreme learning machines (RELMs) are rough-neural networks with one hidden layer where the parameters between the inputs and hidden neurons are arbitrarily chosen and never updated‎. ‎In this paper‎, ‎we propose RELMs with a stable online learning algorithm for the identification of continuous-time nonlinear systems in the presence of noises and uncertainties‎, ‎and we prove the global ...

On Domino Tilings of Rectangles Aztec Diamonds in the Rough

A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers

o enhance the performances of rough-neural networks (R-NNs) in the system identification‎, ‎on the base of emotional learning‎, ‎a new stable learning algorithm is developed for them‎. ‎This algorithm facilitates the error convergence by increasing the memory depth of R-NNs‎. ‎To this end‎, ‎an emotional signal as a linear combination of identification error and its differences is used to achie...

Synthesis of skeletal diamonds: Implications for microdiamond formation in orogenic belts

Figure 1. Secondary-electron scanning electron microscope images showing morphologies of (A) natural and (B, D, and E) synthetic diamonds. C: Energy-dispersive X-ray spectrum for diamond in B. Natural diamonds are separates from felsic gneiss of Kokchetav massif, Kazakhstan. Synthetic diamonds are crystallized from graphite 1 Mg(OH)2 systems and exhibit irregular skeletal morphology similar to ...